Perverse Incentives in ACA’s Risk Adjustment

Introduction

There has been significant discussion in the past several weeks about ACA’s risk adjustors, the New Mexico court decision halting risk adjustment and CMS’ response to that. However, the perverse incentives embedded in the ACA Risk Adjustment process are rarely subject to open discussion. This article discusses these both to enlighten the reader but also present a proposed approach that might benefit the process.

The July 10, 2018 issue of HealthPlanMarkets presented the following chart developed by Axios¹.

Although the chart was identifying the winners and losers if the Trump administration terminates the risk adjustment program, the chart also shows an interesting outcome of the risk adjustment process. Three carriers known in the marketplace for their intense care management efforts and their ability to often deliver a health product at a lower cost than many of their competitors are the ones owing the most as part of the risk adjustment process. In particular, Kaiser owes the most under the current risk adjustment methodology.

Recent analysis suggests that for a typical commercial population Kaiser is able to provide total healthcare to its members in California for less than $400 PMPM². Yet most competitors are unable to offer that same level of coverage for less than $500 PMPM. Some of the cost savings Kaiser offers is a direct result of their efficiency and resource planning efforts. This differentiation is not considered in the current risk adjustment methodology creating a perverse incentive³ to health plans like Kaiser. There is no financial motivation for health plans to pursue becoming more cost effective since it is not recognized by the ACA risk adjustment process. In many of their markets both Molina and Centene, primarily serving the Medicaid population, have the same reputation as Kaiser, a plan that aggressively pursues care management.

AHP regularly measures plan performance and assesses their care management performance. We use a process we have internally developed and express the results in terms of a Care Management Effectiveness Index or CME Index. This process can be simplified to a methodology that reviews four specific utilization indices and determines a CME Index for the plan’s operations. The successful implementation of this type of modification will remove the health plan’s perverse incentive and will motivate more issuers to improve their CME performance and reduce the related cost of health care. This should also attract additional high CME scoring issuers to the marketplace and more of the healthier enrollees.

The proposed methodology involves the following steps:

• Determine average risk adjustment factor from standard CMS methodology
• Develop risk adjusted CME benchmarks by multiplying benchmarks by the average risk adjustment factor
• Compare actual issuer experience to the risk adjusted benchmarks to develop a composite actual/benchmark ratio
• Apply adjustment factors to standard CMS risk score to reflect a performance adjusted CMS risk score
• Continue balance of risk adjustment mechanism to develop risk transfer payments.

We recommend focusing on four key types of utilization benchmarks: days/1,000, ER visits/1,000, office visits/1,000 and scripts/1,000. The benchmark values we have used are consistent with best-in-class CME performance⁴. If, and when an issuer transitions to best-in-class performance, the characteristics of its utilization experience will also transition. Days per thousand and ER visits per thousand will reduce to more efficient levels oftentimes with increases in both the office visit and script rate. We have decided to compare results on a composite basis across all four of these categories. We have developed weights for each metric based upon data from a representative actuarial cost model reflecting the distribution of cost. The proposed benchmark norms are shown below in Table 1.

Table 1 CME Performance Benchmarks

Type of Metric	Weight	Benchmark
Days/1,000	2.25	145
ER Visits/1,000	1.15	85
Office Visits/1,000	0.05	3000
Scripts/1,000	0.05	8500
Total		1.00

Individual health plan experience is compared to the CME Benchmark Norms shown in Table 1 with an overall composite performance calculated. Table 2 presents an illustrative calculation based upon actual data from a major health plan.

Table 2 Actual Health Plan Experience

Type of Metric	Weight	Issuer A
Days/1,000	2.25	245
ER Visits/1,000	1.15	135
Office Visits/1,000	0.05	3000
Scripts/1,000	0.05	9831
Total		1.37

Table 2 shows that the performance prior to risk adjustment was 137% of the CME Benchmark Norms. This would suggest a plan that has a significant potential to improve their performance. Assuming an average risk score of 1.10, this leads to the risk adjusted performance results shown in Table 3 (Raw results divided by Risk Score).

Table 3 Risk Adjusted Performance

Type of Metric	Weight	Issuer A
Days/1,000	2.25	245
ER Visits/1,000	1.15	135
Office Visits/1,000	0.05	3000
Scripts/1,000	0.05	9831
Total		1.37
Risk Score		1.10
Risk Adjusted Performance		1.25

Table 3 shows a risk adjusted performance score of 1.25⁵. This suggests that the overall cost of care for Issuer A is 125% of that achieved by a plan that implemented ideal CME performance.

We have developed a table of risk score adjustment factors based upon the risk adjusted performance observed for each issuer. Table 4 summarizes this adjustment.

Table 4 CME Adjusted Risk Scores

Risk Adjusted CME Score	Risk Adjustment
<1.100	1.050
1.101 – 1.175	1.025
1.176 – 1.250	1.000
1.251 – 1.325	0.975
1.326 – 1.500	0.950
>1.500	0.900

Higher performing plans would have their risk scores increased (smaller risk transfer payments or larger risk transfer receipts) after CME performance adjustment and lower performing plans would have their risk scores decreased (larger risk transfer payments or smaller risk transfer receipts). This modification rewards high performance plans. This assumes a maintenance of budget neutrality as exists today.

Based upon the 2017 risk adjustments shown earlier, I estimate that at least 50% of the “owed” amounts would be reduced by a recognition of CME performance of these health plans. Analysis of 2015 and 2016 payment amounts suggested that almost all of the 2015 adjustment amount and 50% of the 2016 adjustment amount would be eliminated by this enhancement.

Conclusion

ACA risk adjustment is an important feature of health care reform yet the current methodology, although imperfect in many ways, has introduced an extremely unfortunate perverse incentive that actually increases health care costs.

¹https://www.axios.com/newsletters/axios-vitals-8f514fb2-a1f7-40e0-826e-6d59f804eeca.html.

²Total cost of healthcare prior to patient copays, deductibles, and cost sharing.

³A perverse incentive is an incentive that has an unintended and undesirable result which is contrary to the interests of the incentive makers. Perverse incentives are a type of negative unintended consequence or cobra effect.

⁴Benchmarks from the AHP Best Practice Norms^TM.

⁵Factors great than 1.00 suggest performance resulting in higher than average cost. Factors less than 1.00 suggest performance resulting in lower than average cost.

David Axene, FSA, FCA, CERA, MAAA, is the President and Founding Partner of Axene Health Partners, LLC and is based in AHP’s Temecula, CA office.